International Business Machines Corporation

Florian Graf

Andrea Giovannini

Stefan Ravizza

Tim Uwe Scheideler

Erik Rueger

Clea Anne Zolotow

Birgit Monika Pfitzmann

John Valentine Delaney

Xiaolan Zhang

Frederik Frank Flöther

Anthony M Hunt

Kristiann J Schultz

Kamal Bhattacharya

Laxmikantha Sai Nanduru

Marcel Schlatter

Chi Zhang

Praveen Jayachandran

Thorsten Muehge

Thomas Spatzier

Akshat Verma

John Joseph Rofrano

Brian L Peterson

Balaji Viswanathan

Christopher Charles Young

Lakshminarayanan Renganarayana

Rema Ananthanarayanan

Thomas Allen Snellgrove

Maeve M O'Reilly

Gerhard A Widmayer

Petra Sabine Buehrer

Ruediger Schulze

Milton A Bonilla

Shao Yang Yu

Bharat Krishnamurthy

Raphael Waltert

David Kohen

Markus Pelnar

Li Xiao

Naresh Nayar

Mahesh Viswanathan

Thomas E Chefalas

Andrzej P Kochut

Michael Elton Nidd

Xiao Bi Liang

Matthew A Markley

Andrew Paul Barnes

Sean Thomas Fornash

Petra Kopp

Mihai Criveti

Rudolph C Baron

Hsiao Choong Thio

Stefan Mayer

Min Xiang

Richard J Baird

Ricky L Montagna

Pawel J Wieczorek

Marc Albrecht

Avdyl Haxhaj

Simon Streit

Markus Brandes

Robin C Arnold

Dennis M Carden

Rudiger Rissmann

Vladimir V Danilevich

A method includes receiving a first query by a computing device and assigning the first query to a plurality of cognitive engines, wherein each of the plurality of cognitive engines include different characteristics for processing data. The method also includes, responsive to receiving a response from each of the plurality of cognitive engines for the first query, comparing the received responses from the plurality of cognitive engines. The method also included responsive to determining a difference between a first response from a first cognitive engine and a second response from a second cognitive engine is above a predetermined threshold value, performing a response mediation process until the difference is below the predetermined threshold value. The method also includes selecting a first final response from the received responses for the first query and the second query and displaying the first final response to a user.

Cognitive moderator for cognitive instances

Original idea extraction from written data is provided by capturing expression as written text data, obtaining a knowledge graph representing concepts and relationships between the concepts automatically topic modeling the written text data to ascertain thought units and identify respective concepts of the thought unit, mapping a thought unit to the knowledge graph, determining that the thought unit is an original idea, and based on determining that the thought unit is an original idea, storing a representation of the original idea to an idea repository and invoking processing of at least one computer.

Original idea extraction from written text data

A method for linking a first knowledge graph (KG) and a second KG in the presence of a third KG is provided. Content of nodes of the first KG is compared to nodes of the second KG. If a first KG node has a content relationship to a related second KG node, an edge identified by a tuple identifying the first KG and the first KG node and a tuple identifying the second KG and the second KG node is stored in a meta-layer KG. The method comprises comparing content of the nodes from the third KG with the content of nodes from the first and second KG, and in case relationships are identified, more complex tuples establishing this relationship in the meta-layer are stored. Finally, the method also comprises storing at least all nodes and edges of the meta-layer knowledge graph.

Linking and processing different knowledge graphs

A method for event predictions is provided. The method includes receiving input data. The method further includes identifying an object in the input data with the identified object associated with a first node in a knowledge graph. The method further includes determining a second node of a first object event with the second node related to the first node in the knowledge graph. The method further includes contextualizing the identified input object with the first object event.

Detecting and predicting object events from images

A system for enhancing a classifier prediction in respect to underrepresented classes may be provided. A classifier system trained with training data to build a model is used for classifying unknown input data, and an evaluator engine adapted for a determination of an underrepresented class. Additionally, the system comprises an extractor engine adapted for an extraction of relating data from an additional source, and a similarity engine adapted for a selection of data sets out of the relating data wherein the similarity engine is also adapted for comparing features of the relating data and a representative data set for the underrepresented class. Finally, the system comprises a recursion unit adapted for triggering the evaluator engine, the extractor engine and the similarity engine treating selected data set as input data until the evaluator engine classifies the selected data set with a confidence level which is above a confidence threshold level.

Counter rare training date for artificial intelligence

A computer-implemented method, system, and computer program product for dynamic access control to a node in a knowledge graph includes: structuring nodes of a knowledge graph into a plurality of hierarchically organized graph layers; assigning, to one or more users, an access right to a first node of the knowledge graph, the access right to the node selected from a plurality of access rights, where different types of users have different access rights; and assigning, to at least one user from the one or more users, an additional access right to a second node of the knowledge graph.

Dynamic access control for knowledge graph

Methods and systems for datacenter migrations are disclosed. A method includes: virtualizing, by a computing device, servers in a source environment; installing, by the computing device, an isolation firewall to isolate a target environment from the source environment; installing, by the computing device, shared services in the target environment; installing, by the computing device, monitoring and management tools in the virtualized servers in the source environment; replicating, by the computing device, between the source environment and the target environment; and cutting over, by the computing device, from the source environment to the target environment by switching a route advertisement from the source datacenter to the target datacenter.

Big bang approach in datacenter migrations

A computer-implemented method includes: monitoring, by a computing device, computing resource utilization by a plurality of virtual computing components sharing a same kernel; monitoring, by the computing device, transaction counts produced by the plurality of virtual computing components; determining, by the computing device, that the transaction counts increase by less than a threshold number as the computing resource utilization increases; and creating, by the computing device, an additional virtual computing component on a separate kernel based on determining that the transaction counts increase by less than a threshold number as the computing resource utilization increases.

Detecting and counteracting a multiprocessor effect in a virtual computing environment

Original idea extraction from written data is provided by capturing expression as written text data, obtaining a knowledge graph representing concepts and relationships between the concepts automatically topic modeling the written text data to ascertain thought units and identify respective concepts of the thought unit, mapping a thought unit to the knowledge graph, determining that the thought unit is an original idea based on a graph distance in the knowledge graph between correlated concepts represented in the knowledge graph, and based on determining that the thought unit is an original idea, storing a representation of the original idea to an idea repository and invoking processing of at least one computer.

Original idea extraction from written text data using knowledge graphs

A set of workloads to be migrated is identified. A constraint on optimization of the set of workloads is determined. A subset of workloads to be migrated is selected from the set of workloads. A model is constructed, using traffic information corresponding to the set of workloads. The model includes a representation of a relationship between a first workload and a second workload in the subset of workloads. The model is solved to cause generation of a set of optimal flow values. A schedule for a migration wave is constructed. The schedule complies with the constraint on optimization of the set of workloads.

Cloud and datacenter migration wave planning using analytics and flow modeling

A method, computer system, and computer program product for eliminating a redundant node from a knowledge graph is provided. A structural analysis of a knowledge graph is performed by determining that two nodes have a similar structure. An empirical analysis is performed by determining a search result correlation of potentially redundant nodes, said search result correlation comprising a correlation of search result nodes generated from different search queries to said knowledge graph or a correlation of search results due to selected search result nodes in subtrees of said potentially redundant nodes. Results of said structural analysis and said empirical analysis are combined to generate a redundancy confidence level value for two said nodes. One of said two nodes is determined as redundant. One of said two redundant nodes is removed from the knowledge graph.

Identifying redundant nodes in a knowledge graph data structure

A method for off-board data migration. Responsive to receiving a request to migrate a virtual machine image, a memory location of the source virtual machine is identified. Using a generalized pathing interface, a range of logical blocks is accessed for both the source and destination virtual machines. The memory location of the source virtual machine is copied to a memory location of the destination virtual machine. The destination virtual machine is started, and; and the source virtual machine is stopped.

Hardware independent interface for cognitive data migration

Access in a knowledge graph, comprising nodes and edges connecting two or more nodes, is controlled by assigning each node to a non-intersecting zone. A first and second zone identifier, each indicative of a zone occupied by a node where an edge ends, are each stored. Determining an access graph comprising an access node for each zone, access edges representing edges between the zones, and a first list of the zones. Each entry in the first list relates to a zone and a second list of node identifiers, each indicative of a node occupying the zone. A first and second access control list, each related to a zone where an access edge ends and to which a access node in the access graph relates, are stored in each access edge.

Secure zones in knowledge graph

A computer program product, system, and method for building a knowledge graph may include receiving a plurality of new nodes, receiving a base knowledge graph having existing nodes selectively connected by existing edges, and superimposing the new nodes onto selected ones of the existing nodes of the base knowledge graph. The method may further include connecting the new nodes by creating a new edge with a new weight between at least two of the new nodes if corresponding existing nodes in the underlying base knowledge graph have a connection via zero or a predetermined maximum number of existing edges, wherein the new weight is determined based on the existing weights of the existing edges of connections between the corresponding existing nodes, and detaching the new nodes with the new edges from the base knowledge graph.

Structuring incoherent nodes by superimposing on a base knowledge graph

A method and system for deleting a node of a knowledge graph may be provided. The method comprises receiving knowledge graph data comprising nodes and edges, determining a first relevance degree value for a node and a second relevance degree value for the node, combining the first and the second relevance degree value and marking the node as deletable if the final relevance degree value is above a predefined relevance threshold value. The method comprises further applying queries against the knowledge graph, wherein at least a certain number of such queries invokes accessing of a node, marked as deletable, revising at least one of the first and second relevance degree value based on the accessing of the node marked as deletable, and deleting the node, marked as deletable, if over a predetermined period of time queries against the knowledge graph invoke no access of the node marked as deletable.

Knowledge graph node expiration

Growing community of inventors

Zurich, Switzerland

Florian Graf